Breaking down the agglomerative clustering process

In machine learning, unsupervised learning is a machine learning model that infers the data pattern without any guidance or label. Many models are included in the unsupervised learning family, but one of my favorite models is Agglomerative Clustering. Agglomerative Clustering or bottom-up clustering essentially started from an individual cluster (each data point is considered as an individual cluster, also called leaf), then every cluster calculates their distance with each other. The two clusters with the shortest distance with each other would merge creating what we called node. Newly formed clusters once again calculating the member of their cluster distance with another cluster outside of their cluster.